美团【北斗】基于大模型的搜索算法工程师
校招全职核心本地商业-业务研发平台地点:北京 | 上海状态:招聘
任职要求
【任职资格】 1. 2027届硕士及以上学历,计算机、人工智能、机器学习、自然语言处理、信息检索、数学、统计等相关专业优先。 2. 具备扎实的机器学习、深度学习和算法基础,熟悉搜索、推荐、NLP、大模型、多模态理解等至少一个方向。 3. 熟悉 PyTorch / TensorFlow 等深度学习框架,具备较强的编程能力和工程实现能力,能够独立完成算法模块设计、实验验证和线上落地。 4. 对大模型应用有热情,了解或实践过 RAG、Fine-tuning、Agent、Prompt Engineering、Context E…
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工作职责
【愿景】 点评技术部致力于通过Agent 驱动的智能技术平台,深耕真实世界场景的刻画,全面理解用户意图,帮助用户找到、发现与体验真实世界的美好。 【你将参与】 1. 智能搜索核心算法参与大众点评搜索召回、粗排、精排、相关性、混排、Query 理解、地理位置理解等核心算法研发,提升搜索结果的精准性、个性化、时效性和地域匹配能力。 2. 复杂 Query 理解与本地生活语义建模建设面向本地生活场景的意图理解、NER、类目理解、地标识别、本异地识别、场景理解、自然语言条件解析等能力,让系统真正理解用户复杂、模糊、多约束的生活决策需求。 3. AI Native 搜索体验探索用大模型和生成式 AI 重构搜索交互与结果形态,探索评价总结、商户验真、多商户对比、复杂条件找店、LBS 决策、路线规划、攻略问答等新型搜索体验。 4.RAG / Agent / Context Engineering 落地参与构建面向 AI 搜索的检索增强生成系统,包括 Query Planning、多源检索、证据排序、上下文压缩、回答生成、事实校验、Trace 与评估体系等,让大模型在真实业务中稳定、可信、可控地工作。 5. 多模态与本地生活供给理解建设面向商户、评价、笔记、图片、视频、菜品、榜单等多源信息的理解能力,让模型不仅能读懂文本,也能理解真实世界中的场景、环境、菜品、氛围和用户体验。 6. AI 辅助研发与 AutoResearch探索使用 Agent 和 AI 编程工具提升算法研发效率,参与自动化实验、结果分析、badcase 归因、策略迭代等 AI Native 工程实践。
包括英文材料
学历+
机器学习+
https://www.youtube.com/watch?v=0oyDqO8PjIg
Learn about machine learning and AI with this comprehensive 11-hour course from @LunarTech_ai.
https://www.youtube.com/watch?v=i_LwzRVP7bg
Learn Machine Learning in a way that is accessible to absolute beginners.
https://www.youtube.com/watch?v=NWONeJKn6kc
Learn the theory and practical application of machine learning concepts in this comprehensive course for beginners.
https://www.youtube.com/watch?v=PcbuKRNtCUc
Learn about all the most important concepts and terms related to machine learning and AI.
NLP+
https://www.youtube.com/watch?v=fNxaJsNG3-s&list=PLQY2H8rRoyvzDbLUZkbudP-MFQZwNmU4S
Welcome to Zero to Hero for Natural Language Processing using TensorFlow!
https://www.youtube.com/watch?v=R-AG4-qZs1A&list=PLeo1K3hjS3uuvuAXhYjV2lMEShq2UYSwX
Natural Language Processing tutorial for beginners series in Python.
https://www.youtube.com/watch?v=rmVRLeJRkl4&list=PLoROMvodv4rMFqRtEuo6SGjY4XbRIVRd4
The foundations of the effective modern methods for deep learning applied to NLP.
信息检索+
https://nlp.stanford.edu/IR-book/information-retrieval-book.html
Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press. 2008.
深度学习+
https://d2l.ai/
Interactive deep learning book with code, math, and discussions.
算法+
https://roadmap.sh/datastructures-and-algorithms
Step by step guide to learn Data Structures and Algorithms in 2025
https://www.hellointerview.com/learn/code
A visual guide to the most important patterns and approaches for the coding interview.
https://www.w3schools.com/dsa/
大模型+
https://www.youtube.com/watch?v=xZDB1naRUlk
You will build projects with LLMs that will enable you to create dynamic interfaces, interact with vast amounts of text data, and even empower LLMs with the capability to browse the internet for research papers.
https://www.youtube.com/watch?v=zjkBMFhNj_g
PyTorch+
https://datawhalechina.github.io/thorough-pytorch/
PyTorch是利用深度学习进行数据科学研究的重要工具,在灵活性、可读性和性能上都具备相当的优势,近年来已成为学术界实现深度学习算法最常用的框架。
https://www.youtube.com/watch?v=V_xro1bcAuA
Learn PyTorch for deep learning in this comprehensive course for beginners. PyTorch is a machine learning framework written in Python.
TensorFlow+
https://www.youtube.com/watch?v=tpCFfeUEGs8
Ready to learn the fundamentals of TensorFlow and deep learning with Python? Well, you’ve come to the right place.
https://www.youtube.com/watch?v=ZUKz4125WNI
This part continues right where part one left off so get that Google Colab window open and get ready to write plenty more TensorFlow code.
RAG+
https://www.youtube.com/watch?v=sVcwVQRHIc8
Learn how to implement RAG (Retrieval Augmented Generation) from scratch, straight from a LangChain software engineer.
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